System uses conformal prediction to help experts make accurate decisions without understanding when to trust it.
problem Helping experts make accurate decisions in multiclass classification tasks.
method Develops an automated decision support system using conformal prediction to provide precise prediction sets and an efficient search method.
result System improves expert predictions by providing precise prediction sets and forcing experts to predict from these sets.
Improves accuracy and fairness in prediction systems with multiple domain experts.
problem Designing unbiased and accurate deferral systems with multiple experts.
method Proposes a framework for learning a classifier and deferral system that chooses to defer to multiple human experts.
result Significantly improves accuracy and fairness of final predictions compared to baselines.
Expert augmentation improves hybrid model generalization.
problem Limited generalization of hybrid models outside training distribution.
method Introducing expert augmentation to improve hybrid model performance.
result Expert augmentation improves generalization of hybrid models.
Framework allows systems to defer difficult decisions to unknown experts.
problem Ensuring safety and robustness in autonomous systems with unknown human experts.
method Meta-learning approach that adapts to new experts at test-time using optimization and model-based attention mechanisms.
result Framework can quickly adapt deferral policies to new experts without re-training.
One of the key advantages of Inductive Logic Programming systems is the ability of the domain experts to provide background knowledge as modes that allow for efficient search through the space of hypotheses. However, there is an inherent assumption that this expert should also be an ILP expert to provide effective mode…
New decision support systems use prediction sets to help experts update their predictions, improving performance.
problem Challenges in explaining and updating predictions from decision support systems.
method Developed a methodology leveraging nested structure of prediction sets and counterfactual monotonicity to improve performance.
result Limiting experts' agency leads to better performance in decision support systems based on prediction sets.
Learning from demonstration (LfD) is the process of building behavioral models of a task from demonstrations provided by an expert. These models can be used e.g. for system control by generalizing the expert demonstrations to previously unencountered situations. Most LfD methods, however, make strong assumptions about …
CoE modularizes LLMs for scalable, cost-effective AI systems.
problem Challenges in scalability, cost, and customization of monolithic LLMs.
method Composition of Experts (CoE) leverages multiple expert LLMs with a router for dynamic selection.
result CoE achieves superior performance with reduced computational overhead and cost.
Recursive experts improve dynamic sequential learning systems.
problem Adapt to changing environments in sequential learning.
method Design hyper-experts that recursively merge to achieve optimal performance.
result Achieve minimax optimal regret bounds up to constant factors.
End-to-end CAD system for thyroid nodule classification using multimodal data and expert guidance.
problem Improving accuracy in thyroid nodule classification for clinicians.
method Knowledge-driven DenseNet framework using multimodal ultrasound data and expert cues.
result The proposed system achieves relevant performances in thyroid nodule classification.
Expert system predicts credit card charge-offs using macroeconomic indicators.
problem Managing charge-off rates in the credit card industry.
method Developed an expert system using machine learning and macroeconomic indicators.
result Achieved mean squared error values of 1.15E-03 and 1.04E-03.
LHM integrates expert ODEs with neural ODEs for disease progression prediction.
problem Predicting disease progression under medications using limited data.
method Integrating expert-designed ODEs with machine-learned Neural ODEs.
result LHM consistently outperforms previous methods, especially in small sample regimes.
A fuzzy expert system selects stocks for BSE using AI techniques.
problem Selecting stocks for investment allocation is challenging due to many influencing factors.
method Dempster-Shafer (DS) evidence theory for rule base generation, portfolio optimization model with ACO algorithm.
result The model's performance is satisfactory for short-term investment.
We consider a learning system based on the conventional multiplicative weight (MW) rule that combines experts' advice to predict a sequence of true outcomes. It is assumed that one of the experts is malicious and aims to impose the maximum loss on the system. The loss of the system is naturally defined to be the aggreg…
Continuous control imitation learning fails if expert actions are smooth.
problem Continuous control imitation learning fails if expert actions are smooth.
method Study of imitation learning in discrete-time, continuous state-and-action control systems.
result Any smooth, deterministic imitator policy suffers exponentially larger error than the expert.
Develops a real-time exercise recommendation system using deep learning.
problem Improving accuracy in exercise recommendation systems without user feedback.
method Deep recurrent neural network with attention mechanisms, real-time expert feedback.
result Improved accuracy in exercise recommendation system after real-time active learning.
ALPODS AI diagnoses high-dimensional biomedical data with human-understandable explanations.
problem AI decisions in high-dimensional biomedical data are not explainable to humans.
method ALPODS method classifies data based on clusters and generates fuzzy reasoning rules.
result ALPODS provides understandable explanations for AI diagnoses.
A matching in a two-sided market often incurs an externality: a matched resource may become unavailable to the other side of the market, at least for a while. This is especially an issue in online platforms involving human experts as the expert resources are often scarce. The efficient utilization of experts in these p…
New interface explains contextual bandits to non-experts.
problem Interpreting and managing contextual bandits for non-expert operators.
method Developed a metric 'value gain' for off-policy evaluation and designed an interface to explain bandit behavior.
result Empowered non-experts to manage complex machine learning systems through accessible presentation.
A digital twin for multi-scale systems uses physics-based and machine learning models.
problem Lack of application-specific details in digital twin technology.
method Strategically separates into physics-based and data-driven models; uses mixture of experts with Gaussian Process.
result Robust and accurate predictions at future time-steps for multi-scale systems.
NAMEx merges experts using Nash bargaining for improved performance.
problem Sparse Mixture of Experts merging strategies lack a principled weighting mechanism.
method Reinterpreting expert merging through game theory, introducing Nash Merging and complex momentum.
result NAMEx consistently outperforms competing methods across various tasks and system sizes.
ARGUE combines expert networks for anomaly detection.
problem Anomaly detection without labeled data.
method Gated mixture-of-experts architecture combining expert networks.
result Prior knowledge about normal data distribution is valuable.
Reinforcement learning improves online matching by combining expert policies.
problem Efficient decision-making in complex systems like cloud services and marketplaces.
method Combines reinforcement learning with expert policies, using advantage-based weight updates.
result The orchestrated policy converges faster and yields higher efficiency than individual experts and conventional RL.
Data extracted from software repositories is used intensively in Software Engineering research, for example, to predict defects in source code. In our research in this area, with data from open source projects as well as an industrial partner, we noticed several shortcomings of conventional data mining approaches for c…
AlphaZero reveals new chess concepts learnable by top experts.
problem Extracting and understanding hidden knowledge from AI systems.
method Proposed method to extract new chess concepts from AlphaZero.
result Top chess grandmasters show improvements in learning new concepts.
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in…
Budgeted deferral framework reduces expert query costs in machine learning.
problem High cost of querying all experts in deferral algorithms.
method Introduces budgeted deferral framework for selective expert querying.
result Empirical results show substantial reduction in training costs without sacrificing accuracy.
This work improves Gaussian process inference using mixtures of experts and nested SMC samplers.
problem High computational and memory costs of Gaussian processes.
method Mixtures of Gaussian process experts with nested SMC samplers.
result Significantly improved inference compared to importance sampling.
Grading function evaluates Bach-style chorales, outperforming human experts.
problem Difficulty in automatically evaluating musical style correctness.
method Introduces a grading function for evaluating four-part chorales in the style of J.S. Bach.
result Transformer model output is outperformed by the grading function at discriminating Bach chorales.
A new L2D system produces calibrated probabilities of expert correctness without sacrificing accuracy.
problem Calibration of learning to defer systems for safety.
method One-vs-all classifiers with a consistent surrogate loss function.
result Proposes a calibrated L2D system that outperforms existing methods in accuracy and calibration.
YRC-Bench benchmarks AI agents learning to collaborate with experts.
problem Learning to recognize when to consult an expert in novel situations.
method Validation strategy and proposer-validator decomposition.
result Insights for future AI safety research.
When dealing with time series with complex non-stationarities, low retrospective regret on individual realizations is a more appropriate goal than low prospective risk in expectation. Online learning algorithms provide powerful guarantees of this form, and have often been proposed for use with non-stationary processes …
DSelect-k improves MoE models for multi-task learning with better performance and smoother training.
problem Smoothness and convergence issues in sparse gate selection for MoE models.
method Developed DSelect-k, a differentiable and sparse gate for MoE models.
result DSelect-k achieves statistically significant improvements in prediction and expert selection over Top-k.
A new method for multi-expert learning-to-defer avoids optimization issues.
problem Optimization issues in multi-expert learning-to-defer systems.
method A decoupled surrogate with a softmax classifier head and independent sigmoid heads per expert.
result First multi-expert L2D guarantee with a constant not growing with the expert pool.
The paper develops a method for inferring second opinions from experts using counterfactual inference.
problem Designing efficient decision support systems for second opinions.
method Set invariant Gumbel-Max structural causal model for multiclass classification.
result The proposed model can infer second opinions more accurately than non-causal models.
A new method for learning to defer decisions with expert advice improves over standard methods.
problem Learning to defer decisions with expert advice in systems where expert information can be modified after selection.
method An augmented surrogate that operates on the composite expert-advice action space, providing consistency guarantees and excess-risk bounds.
result The method improves over standard Learning-to-Defer and adapts its advice acquisition behavior to the cost regime.
New L2D framework allows deferring specific parts of a sequence prediction to experts.
problem Current L2D methods defer entire predictions, which is not ideal for long sequences.
method Proposes token-level and one-time rejectors to defer specific outputs of a model prediction to experts.
result Granular deferrals achieve better cost-accuracy tradeoffs than whole deferrals.
We consider the forecast aggregation problem in repeated settings, where the forecasts are done on a binary event. At each period multiple experts provide forecasts about an event. The goal of the aggregator is to aggregate those forecasts into a subjective accurate forecast. We assume that experts are Bayesian; namely…
Study on learning to defer to multiple experts with consistent surrogates and confidence calibration.
problem Addressing the open problems of consistent surrogates, confidence calibration, and ensembling of experts.
method Derive two consistent surrogates (softmax and OvA) and propose a conformal inference technique for choosing experts.
result The OvA-based loss does not cause mis-calibration propagation, while the softmax-based loss does.
Solving sequential decision prediction problems, including those in imitation learning settings, requires mitigating the problem of covariate shift. The standard approach, DAgger, relies on capturing expert behaviour in all states that the agent reaches. In real-world settings, querying an expert is costly. We propose …
The article proposes an expert system for detection, and subsequent investigation, of groups of collaborating automobile insurance fraudsters. The system is described and examined in great detail, several technical difficulties in detecting fraud are also considered, for it to be applicable in practice. Opposed to many…
A simple algorithm improves model generalization in expert advice settings.
problem Improving model generalization in expert advice settings.
method A naive aggregation algorithm for point estimations of high-dimensional nonlinear functions.
result Conditions for optimal parameter estimates through sequential mixing distribution strategies.
This study analyzes communication constraints in MoE architectures using information theory.
problem Communication constraints in Mixture-of-Experts (MoE) architectures.
method Developed a rate-distortion characterization of finite-rate gating in MoE architectures using information theory.
result Yielded capacity-aware limits for communication-constrained MoE systems.
AGFN improves causal discovery by integrating expert feedback and handling latent confounding.
problem Inaccurate causal discovery due to unreliable expert knowledge and latent confounding.
method Ancestral GFlowNet (AGFN) is a reinforcement learning algorithm that iteratively refines a policy based on noisy expert feedback to infer ancestral graphs.
result AGFN converges to the true ancestral graph given accurate expert responses and outperforms baselines in structural Hamming distance and Bayesian Information Criterion.
We consider an original problem that arises from the issue of security analysis of a power system and that we name optimal discovery with probabilistic expert advice. We address it with an algorithm based on the optimistic paradigm and on the Good-Turing missing mass estimator. We prove two different regret bounds on t…
Study reduces human labeling in LLM-based classification systems.
problem Minimizing human intervention in training LLM-based classification systems.
method Active learning framework with Conservative Hull-based Classifier (CHC), Center-based Classifier (CC), and Generalized Hull-based Classifier (GHC).
result CHC achieves O(logdT) regret and is minimax optimal for d=1. GHC bridges the gap between different regimes. VAEs struggle with surjective multimodal data, especially class labels describing images.
problem VAEs struggle to capture variability in surjective multimodal data.
method Theoretical and empirical demonstration of VAEs with a mixture of experts posterior.
result VAEs with a mixture of experts posterior can disregard variation in surjective multimodal data.
Specialized hierarchies improve decision-making by partitioning tasks.
problem Division of labor and specialization in hierarchical systems.
method Information-theoretic on-line learning rule for partitioning tasks.
result Enforces specialization and optimal combination of experts.